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Landscape-level analysis of satellite data often requires that pixels be classified. For example, quantifying changes in forest cover across time requires identifying which pixels represent forest. Images can be classified into only a few classes (e.g. forest or non-forest), or many classes representing complex landscapes. Mixed pixels, which record reflectance from more than one cover type, are problematic for classification. In this exercise, you will simulate the spatial resolutions of three different satellite remote sensing platforms: PlanetScope, Sentinel2, and Landsat. By mapping out “pixels” on a heterogeneous landscape, you will demonstrate how changing the spatial resolution of remote sensing imagery affects our ability to classify it. The main goals for the day are a) to experience firsthand the spatial resolution of some global satellite datasets, and b) to understand the limitations of representing complex land cover as a square pixel.
Create your own ‘pixels’ on the ground and observe the landscape features that each one contains.
Add your answers to the table
Once you are done filling out the table by the end of the lab, click the ‘pdf’ button to export your table.
Compare your observations to real satellite imagery of the study area and consider the spectral values of the real pixel corresponding to each site.
Why do you think that the range of NDVI values differs so much between sensors?
What are the brightest and darkest areas in each image?